Relatório Comparativo: Modelos de Classificação

Ir para: Baseline | After Changes | With SMOTE Comparação por Dataset

Modelo 1: Logistic Regression (Baseline) Voltar ao topo

Médias das Métricas

Dataset Accuracy Precision Recall F1-Score ROC AUC
dataset_1000_hypothyroid.csv 0.619 0.092 0.448 0.153 0.541
dataset_1002_ipums_la_98-small.csv 0.527 0.101 0.443 0.165 0.490
dataset_1004_synthetic_control.csv 0.700 0.350 0.933 0.509 0.793
dataset_1013_analcatdata_challenger.csv 0.929 0.000 0.000 0.000 0.500
dataset_1014_analcatdata_dmft.csv 0.433 0.184 0.553 0.277 0.479
dataset_1016_vowel.csv 0.613 0.151 0.704 0.248 0.654
dataset_1018_ipums_la_99-small.csv 0.604 0.047 0.271 0.080 0.449
dataset_1020_mfeat-karhunen.csv 0.567 0.173 0.883 0.290 0.707
dataset_1021_page-blocks.csv 0.859 0.365 0.506 0.424 0.703
dataset_1022_mfeat-pixel.csv 0.562 0.139 0.650 0.229 0.601
dataset_1023_soybean.csv 0.537 0.124 0.393 0.188 0.476
dataset_1039_hiva_agnostic.csv 0.564 0.042 0.511 0.077 0.539
dataset_1045_kc1-top5.csv 0.682 0.125 1.000 0.222 0.833
dataset_1049_pc4.csv 0.678 0.107 0.226 0.145 0.483
dataset_1050_pc3.csv 0.761 0.128 0.229 0.164 0.526
dataset_1056_mc1.csv 0.716 0.001 0.050 0.002 0.385
dataset_1059_ar1.csv 0.568 0.000 0.000 0.000 0.309
dataset_1061_ar4.csv 0.636 0.286 0.667 0.400 0.648
dataset_1064_ar6.csv 0.871 0.667 0.400 0.500 0.681
dataset_1065_kc3.csv 0.630 0.135 0.538 0.215 0.589
dataset_311_oil_spill.csv 0.465 0.057 0.750 0.107 0.601
dataset_312_scene.csv 0.548 0.212 0.566 0.309 0.555
dataset_316_yeast_ml8.csv 0.649 0.023 0.600 0.045 0.625
dataset_38_sick.csv 0.723 0.045 0.174 0.071 0.466
dataset_450_analcatdata_lawsuit.csv 0.575 0.150 1.000 0.261 0.770
dataset_463_backache.csv 0.667 0.238 0.714 0.357 0.687
dataset_757_meta.csv 0.528 0.136 0.688 0.227 0.599
dataset_764_analcatdata_apnea3.csv 0.585 0.100 0.312 0.152 0.467
dataset_765_analcatdata_apnea2.csv 0.615 0.140 0.368 0.203 0.511
dataset_767_analcatdata_apnea1.csv 0.594 0.000 0.000 0.000 0.340
dataset_865_analcatdata_neavote.csv 0.433 0.105 1.000 0.190 0.696
dataset_867_visualizing_livestock.csv 0.718 0.300 0.429 0.353 0.605
dataset_875_analcatdata_chlamydia.csv 0.667 0.250 0.333 0.286 0.542
dataset_940_water-treatment.csv 0.560 0.189 0.583 0.286 0.569
dataset_947_arsenic-male-bladder.csv 0.958 0.000 0.000 0.000 0.500
dataset_949_arsenic-female-bladder.csv 0.613 0.127 0.292 0.177 0.479
dataset_950_arsenic-female-lung.csv 0.988 1.000 0.667 0.800 0.833
dataset_951_arsenic-male-lung.csv 0.458 0.000 0.000 0.000 0.235
dataset_954_spectrometer.csv 0.700 0.218 0.706 0.333 0.703
dataset_958_segment.csv 0.587 0.229 0.798 0.356 0.675
dataset_962_mfeat-morphological.csv 0.742 0.000 0.000 0.000 0.412
dataset_966_analcatdata_halloffame.csv 0.622 0.138 0.595 0.224 0.610
dataset_968_analcatdata_birthday.csv 0.555 0.163 0.500 0.246 0.532
dataset_971_mfeat-fourier.csv 0.547 0.156 0.800 0.261 0.659
dataset_976_JapaneseVowels.csv 0.645 0.268 0.688 0.386 0.663
dataset_978_mfeat-factors.csv 0.595 0.192 0.950 0.319 0.753
dataset_980_optdigits.csv 0.634 0.178 0.715 0.285 0.670
dataset_984_analcatdata_draft.csv 0.791 0.158 0.300 0.207 0.570
dataset_987_collins.csv 0.460 0.139 0.458 0.214 0.459
dataset_995_mfeat-zernike.csv 0.675 0.229 0.950 0.369 0.797

Curvas ROC


dataset_1000_hypothyroid.csv
AUC = 0.541

dataset_1002_ipums_la_98-small.csv
AUC = 0.490

dataset_1004_synthetic_control.csv
AUC = 0.793

dataset_1013_analcatdata_challenger.csv
AUC = 0.500

dataset_1014_analcatdata_dmft.csv
AUC = 0.479

dataset_1016_vowel.csv
AUC = 0.654

dataset_1018_ipums_la_99-small.csv
AUC = 0.449

dataset_1020_mfeat-karhunen.csv
AUC = 0.707

dataset_1021_page-blocks.csv
AUC = 0.703

dataset_1022_mfeat-pixel.csv
AUC = 0.601

dataset_1023_soybean.csv
AUC = 0.476

dataset_1039_hiva_agnostic.csv
AUC = 0.539

dataset_1045_kc1-top5.csv
AUC = 0.833

dataset_1049_pc4.csv
AUC = 0.483

dataset_1050_pc3.csv
AUC = 0.526

dataset_1056_mc1.csv
AUC = 0.385

dataset_1059_ar1.csv
AUC = 0.309

dataset_1061_ar4.csv
AUC = 0.648

dataset_1064_ar6.csv
AUC = 0.681

dataset_1065_kc3.csv
AUC = 0.589

dataset_311_oil_spill.csv
AUC = 0.601

dataset_312_scene.csv
AUC = 0.555

dataset_316_yeast_ml8.csv
AUC = 0.625

dataset_38_sick.csv
AUC = 0.466

dataset_450_analcatdata_lawsuit.csv
AUC = 0.770

dataset_463_backache.csv
AUC = 0.687

dataset_757_meta.csv
AUC = 0.599

dataset_764_analcatdata_apnea3.csv
AUC = 0.467

dataset_765_analcatdata_apnea2.csv
AUC = 0.511

dataset_767_analcatdata_apnea1.csv
AUC = 0.340

dataset_865_analcatdata_neavote.csv
AUC = 0.696

dataset_867_visualizing_livestock.csv
AUC = 0.605

dataset_875_analcatdata_chlamydia.csv
AUC = 0.542

dataset_940_water-treatment.csv
AUC = 0.569

dataset_947_arsenic-male-bladder.csv
AUC = 0.500

dataset_949_arsenic-female-bladder.csv
AUC = 0.479

dataset_950_arsenic-female-lung.csv
AUC = 0.833

dataset_951_arsenic-male-lung.csv
AUC = 0.235

dataset_954_spectrometer.csv
AUC = 0.703

dataset_958_segment.csv
AUC = 0.675

dataset_962_mfeat-morphological.csv
AUC = 0.412

dataset_966_analcatdata_halloffame.csv
AUC = 0.610

dataset_968_analcatdata_birthday.csv
AUC = 0.532

dataset_971_mfeat-fourier.csv
AUC = 0.659

dataset_976_JapaneseVowels.csv
AUC = 0.663

dataset_978_mfeat-factors.csv
AUC = 0.753

dataset_980_optdigits.csv
AUC = 0.670

dataset_984_analcatdata_draft.csv
AUC = 0.570

dataset_987_collins.csv
AUC = 0.459

dataset_995_mfeat-zernike.csv
AUC = 0.797

Modelo 2: Logistic Regression (After Changes) 🔝 Voltar ao topo

Médias das Métricas

Dataset Accuracy Precision Recall F1-Score ROC AUC
dataset_1000_hypothyroid.csv 0.942 1.000 0.241 0.389 0.621
dataset_1002_ipums_la_98-small.csv 0.894 0.438 0.030 0.055 0.513
dataset_1004_synthetic_control.csv 1.000 1.000 1.000 1.000 1.000
dataset_1013_analcatdata_challenger.csv 0.929 0.000 0.000 0.000 0.500
dataset_1014_analcatdata_dmft.csv 0.804 0.000 0.000 0.000 0.500
dataset_1016_vowel.csv 0.946 1.000 0.407 0.579 0.704
dataset_1018_ipums_la_99-small.csv 0.938 0.778 0.041 0.078 0.520
dataset_1020_mfeat-karhunen.csv 0.995 0.983 0.967 0.975 0.982
dataset_1021_page-blocks.csv 0.938 0.924 0.435 0.591 0.715
dataset_1022_mfeat-pixel.csv 0.990 0.909 1.000 0.952 0.994
dataset_1023_soybean.csv 0.937 0.778 0.750 0.764 0.858
dataset_1039_hiva_agnostic.csv 0.965 0.526 0.222 0.312 0.607
dataset_1045_kc1-top5.csv 0.955 0.500 0.500 0.500 0.738
dataset_1049_pc4.csv 0.897 0.750 0.226 0.348 0.608
dataset_1050_pc3.csv 0.896 0.400 0.042 0.075 0.517
dataset_1056_mc1.csv 0.993 0.000 0.000 0.000 0.500
dataset_1059_ar1.csv 0.919 0.000 0.000 0.000 0.500
dataset_1061_ar4.csv 0.848 0.600 0.500 0.545 0.713
dataset_1064_ar6.csv 0.871 1.000 0.200 0.333 0.600
dataset_1065_kc3.csv 0.913 1.000 0.077 0.143 0.538
dataset_311_oil_spill.csv 0.965 1.000 0.167 0.286 0.583
dataset_312_scene.csv 0.943 0.844 0.837 0.840 0.902
dataset_316_yeast_ml8.csv 0.986 0.000 0.000 0.000 0.500
dataset_38_sick.csv 0.940 0.600 0.043 0.081 0.521
dataset_450_analcatdata_lawsuit.csv 0.950 1.000 0.333 0.500 0.667
dataset_463_backache.csv 0.889 0.667 0.286 0.400 0.632
dataset_757_meta.csv 0.881 0.000 0.000 0.000 0.490
dataset_764_analcatdata_apnea3.csv 0.919 0.857 0.375 0.522 0.683
dataset_765_analcatdata_apnea2.csv 0.930 1.000 0.474 0.643 0.737
dataset_767_analcatdata_apnea1.csv 0.909 0.727 0.444 0.552 0.710
dataset_865_analcatdata_neavote.csv 0.933 0.000 0.000 0.000 0.500
dataset_867_visualizing_livestock.csv 0.872 0.667 0.571 0.615 0.754
dataset_875_analcatdata_chlamydia.csv 0.933 0.833 0.833 0.833 0.896
dataset_940_water-treatment.csv 0.881 1.000 0.208 0.345 0.604
dataset_947_arsenic-male-bladder.csv 0.976 1.000 0.429 0.600 0.714
dataset_949_arsenic-female-bladder.csv 0.869 0.750 0.125 0.214 0.559
dataset_950_arsenic-female-lung.csv 0.982 1.000 0.500 0.667 0.750
dataset_951_arsenic-male-lung.csv 0.994 1.000 0.750 0.857 0.875
dataset_954_spectrometer.csv 0.962 1.000 0.647 0.786 0.824
dataset_958_segment.csv 0.916 0.918 0.455 0.608 0.724
dataset_962_mfeat-morphological.csv 0.998 1.000 0.983 0.992 0.992
dataset_966_analcatdata_halloffame.csv 0.960 0.889 0.649 0.750 0.820
dataset_968_analcatdata_birthday.csv 0.882 0.714 0.312 0.435 0.646
dataset_971_mfeat-fourier.csv 0.997 0.968 1.000 0.984 0.998
dataset_976_JapaneseVowels.csv 0.950 0.888 0.789 0.836 0.885
dataset_978_mfeat-factors.csv 0.987 0.882 1.000 0.938 0.993
dataset_980_optdigits.csv 0.982 0.938 0.884 0.910 0.939
dataset_984_analcatdata_draft.csv 0.909 0.000 0.000 0.000 0.500
dataset_987_collins.csv 0.900 0.667 0.750 0.706 0.839
dataset_995_mfeat-zernike.csv 0.995 0.967 0.983 0.975 0.990

Curvas ROC


dataset_1000_hypothyroid.csv
AUC = 0.621

dataset_1002_ipums_la_98-small.csv
AUC = 0.513

dataset_1004_synthetic_control.csv
AUC = 1.000

dataset_1013_analcatdata_challenger.csv
AUC = 0.500

dataset_1014_analcatdata_dmft.csv
AUC = 0.500

dataset_1016_vowel.csv
AUC = 0.704

dataset_1018_ipums_la_99-small.csv
AUC = 0.520

dataset_1020_mfeat-karhunen.csv
AUC = 0.982

dataset_1021_page-blocks.csv
AUC = 0.715

dataset_1022_mfeat-pixel.csv
AUC = 0.994

dataset_1023_soybean.csv
AUC = 0.858

dataset_1039_hiva_agnostic.csv
AUC = 0.607

dataset_1045_kc1-top5.csv
AUC = 0.738

dataset_1049_pc4.csv
AUC = 0.608

dataset_1050_pc3.csv
AUC = 0.517

dataset_1056_mc1.csv
AUC = 0.500

dataset_1059_ar1.csv
AUC = 0.500

dataset_1061_ar4.csv
AUC = 0.713

dataset_1064_ar6.csv
AUC = 0.600

dataset_1065_kc3.csv
AUC = 0.538

dataset_311_oil_spill.csv
AUC = 0.583

dataset_312_scene.csv
AUC = 0.902

dataset_316_yeast_ml8.csv
AUC = 0.500

dataset_38_sick.csv
AUC = 0.521

dataset_450_analcatdata_lawsuit.csv
AUC = 0.667

dataset_463_backache.csv
AUC = 0.632

dataset_757_meta.csv
AUC = 0.490

dataset_764_analcatdata_apnea3.csv
AUC = 0.683

dataset_765_analcatdata_apnea2.csv
AUC = 0.737

dataset_767_analcatdata_apnea1.csv
AUC = 0.710

dataset_865_analcatdata_neavote.csv
AUC = 0.500

dataset_867_visualizing_livestock.csv
AUC = 0.754

dataset_875_analcatdata_chlamydia.csv
AUC = 0.896

dataset_940_water-treatment.csv
AUC = 0.604

dataset_947_arsenic-male-bladder.csv
AUC = 0.714

dataset_949_arsenic-female-bladder.csv
AUC = 0.559

dataset_950_arsenic-female-lung.csv
AUC = 0.750

dataset_951_arsenic-male-lung.csv
AUC = 0.875

dataset_954_spectrometer.csv
AUC = 0.824

dataset_958_segment.csv
AUC = 0.724

dataset_962_mfeat-morphological.csv
AUC = 0.992

dataset_966_analcatdata_halloffame.csv
AUC = 0.820

dataset_968_analcatdata_birthday.csv
AUC = 0.646

dataset_971_mfeat-fourier.csv
AUC = 0.998

dataset_976_JapaneseVowels.csv
AUC = 0.885

dataset_978_mfeat-factors.csv
AUC = 0.993

dataset_980_optdigits.csv
AUC = 0.939

dataset_984_analcatdata_draft.csv
AUC = 0.500

dataset_987_collins.csv
AUC = 0.839

dataset_995_mfeat-zernike.csv
AUC = 0.990

Modelo 3: Logistic Regression (With SMOTE) 🔝 Voltar ao topo

Médias das Métricas

Dataset Accuracy Precision Recall F1-Score ROC AUC
dataset_1000_hypothyroid.csv 0.746 0.209 0.828 0.334 0.905
dataset_1002_ipums_la_98-small.csv 0.650 0.230 0.992 0.374 0.840
dataset_1004_synthetic_control.csv 0.989 0.938 1.000 0.968 1.000
dataset_1013_analcatdata_challenger.csv 0.619 0.067 0.333 0.111 0.667
dataset_1014_analcatdata_dmft.csv 0.529 0.216 0.532 0.307 0.506
dataset_1016_vowel.csv 0.865 0.403 1.000 0.574 0.976
dataset_1018_ipums_la_99-small.csv 0.651 0.151 0.959 0.260 0.881
dataset_1020_mfeat-karhunen.csv 0.932 0.594 1.000 0.745 1.000
dataset_1021_page-blocks.csv 0.880 0.455 0.881 0.600 0.939
dataset_1022_mfeat-pixel.csv 0.918 0.550 1.000 0.710 1.000
dataset_1023_soybean.csv 0.893 0.562 0.964 0.711 0.980
dataset_1039_hiva_agnostic.csv 0.870 0.167 0.667 0.267 0.770
dataset_1045_kc1-top5.csv 0.841 0.143 0.500 0.222 0.929
dataset_1049_pc4.csv 0.733 0.295 0.868 0.440 0.859
dataset_1050_pc3.csv 0.701 0.226 0.792 0.352 0.825
dataset_1056_mc1.csv 0.825 0.030 0.750 0.057 0.927
dataset_1059_ar1.csv 0.622 0.133 0.667 0.222 0.843
dataset_1061_ar4.csv 0.606 0.267 0.667 0.381 0.790
dataset_1064_ar6.csv 0.613 0.182 0.400 0.250 0.585
dataset_1065_kc3.csv 0.746 0.238 0.769 0.364 0.845
dataset_311_oil_spill.csv 0.862 0.186 0.667 0.291 0.902
dataset_312_scene.csv 0.860 0.564 0.961 0.711 0.966
dataset_316_yeast_ml8.csv 0.814 0.063 0.900 0.118 0.910
dataset_38_sick.csv 0.784 0.209 0.913 0.341 0.928
dataset_450_analcatdata_lawsuit.csv 0.838 0.316 1.000 0.480 1.000
dataset_463_backache.csv 0.759 0.312 0.714 0.435 0.726
dataset_757_meta.csv 0.629 0.159 0.625 0.253 0.746
dataset_764_analcatdata_apnea3.csv 0.748 0.312 0.938 0.469 0.936
dataset_765_analcatdata_apnea2.csv 0.923 0.667 0.842 0.744 0.878
dataset_767_analcatdata_apnea1.csv 0.643 0.254 0.944 0.400 0.920
dataset_865_analcatdata_neavote.csv 0.433 0.105 1.000 0.190 0.696
dataset_867_visualizing_livestock.csv 0.846 0.571 0.571 0.571 0.790
dataset_875_analcatdata_chlamydia.csv 0.867 0.600 1.000 0.750 0.965
dataset_940_water-treatment.csv 0.730 0.327 0.750 0.456 0.819
dataset_947_arsenic-male-bladder.csv 0.714 0.113 0.857 0.200 0.841
dataset_949_arsenic-female-bladder.csv 0.649 0.278 0.917 0.427 0.814
dataset_950_arsenic-female-lung.csv 0.881 0.182 0.667 0.286 0.832
dataset_951_arsenic-male-lung.csv 1.000 1.000 1.000 1.000 1.000
dataset_954_spectrometer.csv 0.806 0.354 1.000 0.523 0.983
dataset_958_segment.csv 0.818 0.440 1.000 0.611 0.989
dataset_962_mfeat-morphological.csv 0.935 0.608 0.983 0.752 0.989
dataset_966_analcatdata_halloffame.csv 0.816 0.324 0.919 0.479 0.966
dataset_968_analcatdata_birthday.csv 0.827 0.457 1.000 0.627 0.949
dataset_971_mfeat-fourier.csv 0.967 0.750 1.000 0.857 1.000
dataset_976_JapaneseVowels.csv 0.868 0.550 0.996 0.709 0.986
dataset_978_mfeat-factors.csv 0.935 0.606 1.000 0.755 1.000
dataset_980_optdigits.csv 0.897 0.498 0.959 0.656 0.990
dataset_984_analcatdata_draft.csv 0.455 0.097 0.600 0.167 0.491
dataset_987_collins.csv 0.793 0.434 0.958 0.597 0.952
dataset_995_mfeat-zernike.csv 0.925 0.571 1.000 0.727 1.000

Curvas ROC


dataset_1000_hypothyroid.csv
AUC = 0.905

dataset_1002_ipums_la_98-small.csv
AUC = 0.840

dataset_1004_synthetic_control.csv
AUC = 1.000

dataset_1013_analcatdata_challenger.csv
AUC = 0.667

dataset_1014_analcatdata_dmft.csv
AUC = 0.506

dataset_1016_vowel.csv
AUC = 0.976

dataset_1018_ipums_la_99-small.csv
AUC = 0.881

dataset_1020_mfeat-karhunen.csv
AUC = 1.000

dataset_1021_page-blocks.csv
AUC = 0.939

dataset_1022_mfeat-pixel.csv
AUC = 1.000

dataset_1023_soybean.csv
AUC = 0.980

dataset_1039_hiva_agnostic.csv
AUC = 0.770

dataset_1045_kc1-top5.csv
AUC = 0.929

dataset_1049_pc4.csv
AUC = 0.859

dataset_1050_pc3.csv
AUC = 0.825

dataset_1056_mc1.csv
AUC = 0.927

dataset_1059_ar1.csv
AUC = 0.843

dataset_1061_ar4.csv
AUC = 0.790

dataset_1064_ar6.csv
AUC = 0.585

dataset_1065_kc3.csv
AUC = 0.845

dataset_311_oil_spill.csv
AUC = 0.902

dataset_312_scene.csv
AUC = 0.966

dataset_316_yeast_ml8.csv
AUC = 0.910

dataset_38_sick.csv
AUC = 0.928

dataset_450_analcatdata_lawsuit.csv
AUC = 1.000

dataset_463_backache.csv
AUC = 0.726

dataset_757_meta.csv
AUC = 0.746

dataset_764_analcatdata_apnea3.csv
AUC = 0.936

dataset_765_analcatdata_apnea2.csv
AUC = 0.878

dataset_767_analcatdata_apnea1.csv
AUC = 0.920

dataset_865_analcatdata_neavote.csv
AUC = 0.696

dataset_867_visualizing_livestock.csv
AUC = 0.790

dataset_875_analcatdata_chlamydia.csv
AUC = 0.965

dataset_940_water-treatment.csv
AUC = 0.819

dataset_947_arsenic-male-bladder.csv
AUC = 0.841

dataset_949_arsenic-female-bladder.csv
AUC = 0.814

dataset_950_arsenic-female-lung.csv
AUC = 0.832

dataset_951_arsenic-male-lung.csv
AUC = 1.000

dataset_954_spectrometer.csv
AUC = 0.983

dataset_958_segment.csv
AUC = 0.989

dataset_962_mfeat-morphological.csv
AUC = 0.989

dataset_966_analcatdata_halloffame.csv
AUC = 0.966

dataset_968_analcatdata_birthday.csv
AUC = 0.949

dataset_971_mfeat-fourier.csv
AUC = 1.000

dataset_976_JapaneseVowels.csv
AUC = 0.986

dataset_978_mfeat-factors.csv
AUC = 1.000

dataset_980_optdigits.csv
AUC = 0.990

dataset_984_analcatdata_draft.csv
AUC = 0.491

dataset_987_collins.csv
AUC = 0.952

dataset_995_mfeat-zernike.csv
AUC = 1.000


🔍 Comparação por Dataset

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